def create_report_delayed(exp_id, delayed, description):
delays = numpy.array(sorted(delayed.keys()))
r = Report(exp_id)
r.text("description", description)
f = r.figure(cols=3)
# max and sum of correlation for each delay
# corr_max = []
corr_mean = []
for delay in delays:
data = delayed[delay]
a = data["action_image_correlation"]
id = "delay%d" % delay
# rr = r.node('delay%d' % delay)
r.data(id, a).data_rgb("retina", add_reflines(posneg(values2retina(a))))
corr_mean.append(numpy.abs(a).mean())
caption = "delay: %d (max: %.3f, sum: %f)" % (delay, numpy.abs(a).max(), numpy.abs(a).sum())
f.sub(id, caption=caption)
timestamp2ms = lambda x: x * (1.0 / 60) * 1000
peak = numpy.argmax(corr_mean)
peak_ms = timestamp2ms(delays[peak])
with r.data_pylab("mean") as pylab:
T = timestamp2ms(delays)
pylab.plot(T, corr_mean, "o-")
pylab.ylabel("mean correlation field")
pylab.xlabel("delay (ms) ")
a = pylab.axis()
pylab.plot([0, 0], [a[2], a[3]], "k-")
y = a[2] + (a[3] - a[2]) * 0.1
pylab.text(+5, y, "causal", horizontalalignment="left")
pylab.text(-5, y, "non causal", horizontalalignment="right")
pylab.plot([peak_ms, peak_ms], [a[2], max(corr_mean)], "b--")
y = a[2] + (a[3] - a[2]) * 0.2
pylab.text(peak_ms + 10, y, "%d ms" % peak_ms, horizontalalignment="left")
f = r.figure("stats")
f.sub("mean")
a = delayed[int(delays[peak])]["action_image_correlation"]
r.data_rgb("best_delay", add_reflines(posneg(values2retina(a))))
return r
def pub_tensor2(pub, name, V):
""" Publishes a generic 2D tensor """
with pub.subsection(name) as section:
section.array("value", V) # TODO
value_rgb = posneg(V)
pub_save_versions(section, value_rgb)
pub_stats(section, V)
def publish_ros_commands(self, commands):
from reprep import posneg
# commands = commands.reshape((1, commands.size))
z = 4
commands = np.kron(commands, np.ones((z, z)))
commands_image = posneg(commands)
ros_image = numpy_to_imgmsg(commands_image, stamp=None)
self.pub_commands_image.publish(ros_image)
def pub_tensor3_slice2(pub, name, V):
""" Displays a 3D tensor, with shape [nxnxk] """
with pub.subsection(name) as section:
section.array("value", V)
nslices = V.shape[2]
max_value = np.nanmax(np.abs(V))
if not np.isfinite(max_value):
msg = "Expected a finite value."
raise ValueError(msg)
fu = section.figure("unnormalized", cols=nslices)
fn = section.figure("normalized", cols=nslices)
fug = section.figure("unnormalizedg", cols=nslices)
fng = section.figure("normalizedg", cols=nslices)
with section.subsection("slices") as slices:
for i in range(nslices):
tslice = V[:, :, i]
with slices.subsection("%d" % i) as s:
s.array("value", tslice)
rgbu = posneg(tslice)
rgbn = posneg(tslice, max_value=max_value)
dn = pub_save_versions2(s, "normalized", rgbn)
fn.sub(dn)
du = pub_save_versions2(s, "unnormalized", rgbu)
fu.sub(du)
gray_u = posneg_hinton(tslice)
gray_n = posneg_hinton(tslice, max_value=max_value)
dng = pub_save_versions2(s, "normalizedg", gray_n)
fug.sub(dng)
dug = pub_save_versions2(s, "unnormalizedg", gray_u)
fng.sub(dug)
pub_stats(section, V)
def add_posneg(report, id, x, **kwargs):
n = report.data(id, x)
n.data_rgb('retina',
add_reflines(posneg(values2retina(x), **kwargs)))
#with n.data_pylab('plot') as pylab:
# pylab.plot(x, '.')
return n
def pub_tensor2_cov(pub, name, V, rcond=None):
""" Publishes a tensor which is supposed to represent a covariance. """
with pub.subsection(name) as sub:
sub.array_as_image("posneg", V) # XXX: redundant, but don't want to change code
sub.array("value", V)
rgb = posneg(V)
pub_save_versions(sub, rgb)
pub_svd_decomp(sub, V, rcond=rcond)
pub_stats(sub, V)
def write_image(name, values):
time_us = timestamp * 1000 * 1000
dirname2 = os.path.join(dirname, name)
if not os.path.exists(dirname2):
os.makedirs(dirname2)
filename = os.path.join(dirname2, 'ms%08d.png' % time_us)
if np.nanmin(values) < 0:
rgb = posneg(values)
else:
rgb = scale(values)
rgb = rgb_zoom(rgb, K=4)
imwrite(rgb, filename)
logger.debug('print to %r' % filename)
def write_image(name, values):
time_us = timestamp * 1000 * 1000
dirname2 = os.path.join(dirname, name)
if not os.path.exists(dirname2):
os.makedirs(dirname2)
filename = os.path.join(dirname2, 'ms%08d.png' % time_us)
if np.nanmin(values) < 0:
rgb = posneg(values)
else:
rgb = scale(values)
rgb = rgb_zoom(rgb, K=4)
imwrite(rgb, filename)
logger.debug('print to %r' % filename)
def get_rgb(field, colors, **kwargs):
''' Returns a dict with values 'rgb' (rgb in [0,1]), 'colorbar', etc.. '''
properties = {}
if colors == 'posneg':
rgb = posneg(field, properties=properties, **kwargs) / 255.0
elif colors == 'scale':
rgb = scale(field, properties=properties, **kwargs) / 255.0
elif colors == 'cmap':
rgb = filter_colormap(field,
properties=properties, **kwargs) / 255.0
else:
raise ValueError('No known colors %r. ' % colors)
properties['rgb'] = rgb
return properties
def template_bds_P(frag, id_set, id_robot, id_agent, width="3cm"):
report = load_report_phase(id_set=id_set, agent=id_agent, robot=id_robot, phase="learn")
gid = "%s-%s-%s-P" % (id_set, id_robot, id_agent)
V = report["estimator/tensors/P/value"].raw_data
n = V.shape[0]
rgb = posneg(V)
if n > 50:
frag.save_graphics_data(jpg_data(rgb), MIME_JPG, gid)
else:
rgb = rgb_zoom(rgb, 16)
frag.save_graphics_data(png_data(rgb), MIME_PNG, gid)
# frag.save_graphics_data(node.raw_data, node.mime, gid)
tensor_figure(frag, gid=gid, xlabel="s", ylabel="v", width=width, label="\TPe^{sv}")
def add_pieces(sub, V, n, zoom=16):
pieces = get_pieces(V, n)
for k, piece in enumerate(pieces):
with sub.subsection("%d" % k) as subk:
value = piece["value"]
rgb = posneg(value)
rgb = rgb_zoom(rgb, zoom)
# XXX
subk.r.data_rgb("png", rgb, mime=MIME_PNG)
rel = subk.section("rel")
rel.r.data("x", piece["xfrac"])
rel.r.data("y", piece["yfrac"])
rel.r.data("width", piece["wfrac"])
rel.r.data("height", piece["wfrac"])
pixels = subk.section("pixels")
pixels.r.data("x", piece["x"])
pixels.r.data("y", piece["y"])
pixels.r.data("width", piece["w"])
pixels.r.data("height", piece["h"])
def diffeo_to_rgb_curv(D):
stats = diffeo_stats(D)
return posneg(stats.curv)
def render(x):
return posneg(x, skim=skim)
def diffeo_to_rgb_curv(D):
stats = diffeo_stats(D)
return posneg(stats.curv)
def draw_correlation_figure(pylab, xlabels, ylabels, R):
rgb = posneg(R, max_value=1)
draw_matrix_and_labels(pylab, xlabels, ylabels, rgb)
